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Found 95 papers, 65 papers with code
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases
Complex question generation over knowledge bases (KB) aims to generate natural language questions involving multiple KB relations or functional constraints.
MIGE: A Unified Framework for Multimodal Instruction-Based Image Generation and Editing
Experiments show that MIGE excels in both subject-driven generation and instruction-based editing while setting a state-of-the-art in the new task of instruction-based subject-driven editing.
Revisiting Robust RAG: Do We Still Need Complex Robust Training in the Era of Powerful LLMs?
Our findings suggest that RAG systems can benefit from simpler architectures and training strategies as models become more powerful, enabling more scalable applications with minimal complexity.
Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment
The conditional distribution alignment task focuses on aligning text embeddings with positive samples embeddings by leveraging the conditional distribution of embeddings while simultaneously reducing the likelihood of generating negative samples from text embeddings, thereby achieving embedding alignment and uniformity.
ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models
Tool learning has emerged as a crucial capability for large language models (LLMs) to solve complex real-world tasks through interaction with external tools.
The Mirage of Model Editing: Revisiting Evaluation in the Wild
Our analysis provides a fundamental reexamination of both the real-world applicability of existing model editing methods and their evaluation practices, and establishes a rigorous evaluation framework with key insights to advance reliable and practical model editing research.
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos
Video information retrieval remains a fundamental approach for accessing video content.
Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject
Knowledge editing has become a promising approach for efficiently and precisely updating knowledge embedded in large language models (LLMs).
Personalized Denoising Implicit Feedback for Robust Recommender System
To tackle these challenges, we further investigate the loss overlap and find that for a given user, there is a clear distinction between normal and noisy interactions in the user's personal loss distribution.
Fact-Level Confidence Calibration and Self-Correction
Confidence calibration in LLMs, i. e., aligning their self-assessed confidence with the actual accuracy of their responses, enabling them to self-evaluate the correctness of their outputs.
Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation
Adversarial Collaborative Filtering (ACF), which typically applies adversarial perturbations at user and item embeddings through adversarial training, is widely recognized as an effective strategy for enhancing the robustness of Collaborative Filtering (CF) recommender systems against poisoning attacks.
Cross-Modal Safety Mechanism Transfer in Large Vision-Language Models
Vision-language alignment in Large Vision-Language Models (LVLMs) successfully enables LLMs to understand visual input.
Ranked #220 on
Visual Question Answering
on MM-Vet
MITA: Bridging the Gap between Model and Data for Test-time Adaptation
Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models.
Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration
In this paper, we introduce a novel TDA method called Debias and Denoise Attribution (DDA), which enhances influence functions by addressing fitting errors.
Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System
Leveraging these insights, we introduce the Vulnerability-aware Adversarial Training (VAT), designed to defend against poisoning attacks in recommender systems.
Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems
Recent studies have demonstrated the vulnerability of recommender systems to data poisoning attacks, where adversaries inject carefully crafted fake user interactions into the training data of recommenders to promote target items.
ALiiCE: Evaluating Positional Fine-grained Citation Generation
Large Language Models (LLMs) can enhance the credibility and verifiability by generating text with citations.
Understanding the Collapse of LLMs in Model Editing
Despite significant progress in model editing methods, their application in real-world scenarios remains challenging as they often cause large language models (LLMs) to collapse.
A Theory for Token-Level Harmonization in Retrieval-Augmented Generation
Based on our theory, we propose a practical novel method, Tok-RAG, which achieves collaborative generation between the pure LLM and RAG at token level to preserve benefit and avoid detriment.
Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning
Graph contrastive learning (GCL), standing as the dominant paradigm in the realm of graph pre-training, has yielded considerable progress.
Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models
Firstly, in the layer dimension, we propose non-local block key-value storage to replace local layer key-value storage, increasing the representation ability of key-value pairs and incorporating attention layer knowledge.
When to Trust LLMs: Aligning Confidence with Response Quality
Specifically, the order-preserving reward incentivizes the model to verbalize greater confidence for responses of higher quality to align the order of confidence and quality.
Do LLMs Play Dice? Exploring Probability Distribution Sampling in Large Language Models for Behavioral Simulation
With the rapid advancement of large language models (LLMs) for handling complex language tasks, an increasing number of studies are employing LLMs as agents to emulate the sequential decision-making processes of humans often represented as Markov decision-making processes (MDPs).
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models
We evaluate the multilingual knowledge editing generalization capabilities of existing methods on MLaKE.
Knowledge Boundary and Persona Dynamic Shape A Better Social Media Agent
For dynamic persona information, we use current action information to internally retrieve the persona information of the agent, thereby reducing the interference of diverse persona information on the current action.
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating additional information from retrieval.
Unlocking the Power of Large Language Models for Entity Alignment
To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy.
Qsnail: A Questionnaire Dataset for Sequential Question Generation
Questionnaires entail a series of questions that must conform to intricate constraints involving the questions, options, and overall structure.
Improving Video Corpus Moment Retrieval with Partial Relevance Enhancement
We argue that effectively capturing the partial relevance between the query and video is essential for the VCMR task.
Event-aware Video Corpus Moment Retrieval
Video Corpus Moment Retrieval (VCMR) is a practical video retrieval task focused on identifying a specific moment within a vast corpus of untrimmed videos using the natural language query.
Stable Knowledge Editing in Large Language Models
The premise of localization results in an incomplete knowledge editing, whereas an isolated assumption may impair both other knowledge and general abilities.
Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models
A careful and balanced integration of the parametric knowledge within LLMs with external information is crucial to alleviate hallucinations.
Unlink to Unlearn: Simplifying Edge Unlearning in GNNs
As concerns over data privacy intensify, unlearning in Graph Neural Networks (GNNs) has emerged as a prominent research frontier in academia.
List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation
First, it is hard to share the contextual information of the ranking list between the two tasks.
Graph Domain Adaptation: Challenges, Progress and Prospects
To the best of our knowledge, this paper is the first survey for graph domain adaptation.
LoRec: Large Language Model for Robust Sequential Recommendation against Poisoning Attacks
Traditional defense strategies predominantly depend on predefined assumptions or rules extracted from specific known attacks, limiting their generalizability to unknown attack types.
TEA: Test-time Energy Adaptation
To address this, we propose a novel energy-based perspective, enhancing the model's perception of target data distributions without requiring access to training data or processes.
Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images
Furthermore, our subsequent exploration reveals that the inclusion of AI-generated images in the training data of the retrieval models exacerbates the invisible relevance bias.
Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue
The emergence of large language models (LLMs) further improves the capabilities of open-domain dialogue systems and can generate fluent, coherent, and diverse responses.
Plot Retrieval as an Assessment of Abstract Semantic Association
Readers usually only give an abstract and vague description as the query based on their own understanding, summaries, or speculations of the plot, which requires the retrieval model to have a strong ability to estimate the abstract semantic associations between the query and candidate plots.
DyTSCL: Dynamic graph representation via tempo-structural contrastive learning
In this paper, we propose a novel Dynamic graph representation framework via Tempo-Structural Contrastive Learning, DyTSCL, which trains the model by identifying three different subgraphs as a task, named Tempo-Structural subgraph, Non-Temporal subgraph and Non-Structural subgraph.
RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling
It encodes the text corpus into a latent space, capturing current and future information from both source and target text.
Causality and Independence Enhancement for Biased Node Classification
Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias.
Robust Recommender System: A Survey and Future Directions
This survey provides a comprehensive review of recent work on recommender systems' robustness.
Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer
Transfer learning has been a feasible way to transfer knowledge from high-quality external data of source domains to limited data of target domains, which follows a domain-level knowledge transfer to learn a shared posterior distribution.
OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning
Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark for evaluating graph domain adaptation models.
PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion
The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones.
IDEA: Invariant Defense for Graph Adversarial Robustness
To address these limitations, we analyze the causalities in graph adversarial attacks and conclude that causal features are key to achieve graph adversarial robustness, owing to their determinedness for labels and invariance across attacks.
LLMDet: A Third Party Large Language Models Generated Text Detection Tool
By jointly analyzing the proxy perplexities of LLMs, we can determine the source of the generated text.
SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation
Language models trained on large-scale corpora can generate remarkably fluent results in open-domain dialogue.
BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval
Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets.
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
In this paper, we propose a new criterion for popularity debiasing, i. e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion.
Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks
This paper proposes a novel framework named \textbf{Search-in-the-Chain} (SearChain) for the interaction between LLM and IR to solve the challenges.
Ranked #2 on
Question Answering
on StrategyQA
(EM metric)
Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets
Firstly, we address the oversimplified heterogeneity settings of current datasets and propose two new HHKG datasets that closely mimic practical EA scenarios.
Graph Adversarial Immunization for Certifiable Robustness
In this paper, we propose and formulate graph adversarial immunization, i. e., vaccinating part of graph structure to improve certifiable robustness of graph against any admissible adversarial attack.
Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation
To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance.
Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network
To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes.
Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks
However, most nodes in the tribe-style graph lack attributes, making it difficult to directly adopt existing graph learning methods (e. g., Graph Neural Networks(GNNs)).
Multi-video Moment Ranking with Multimodal Clue
In this paper, we focus on improving two problems of two-stage method: (1) Moment prediction bias: The predicted moments for most queries come from the top retrieved videos, ignoring the possibility that the target moment is in the bottom retrieved videos, which is caused by the inconsistency of Shared Normalization during training and inference.
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding
Comparative loss is essentially a ranking loss on top of the task-specific losses of the full and ablated models, with the expectation that the task-specific loss of the full model is minimal.
NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework
Different needs correspond to different IR tasks such as document retrieval, open-domain question answering, retrieval-based dialogue, etc., while they share the same schema to estimate the relationship between texts.
Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective
Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks.
Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network
On account that previous studies either lacks variance analysis or only focus on a particular sampling paradigm, we firstly propose an unified node sampling variance analysis framework and analyze the core challenge "circular dependency" for deriving the minimum variance sampler, i. e., sampling probability depends on node embeddings while node embeddings can not be calculated until sampling is finished.
DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph
Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years.
Adversarial Camouflage for Node Injection Attack on Graphs
In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes.
Learning node embeddings via summary graphs: a brief theoretical analysis
Recent works try to improve scalability via graph summarization -- i. e., they learn embeddings on a smaller summary graph, and then restore the node embeddings of the original graph.
Few-Shot Stance Detection via Target-Aware Prompt Distillation
The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets.
Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting.
LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback
Ideally, if a PRF model can distinguish between irrelevant and relevant information in the feedback, the more feedback documents there are, the better the revised query will be.
Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors
Given a multivariate big time series, can we detect anomalies as soon as they occur?
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning
In generalization stage, matching model explores the essential matching signals by being trained on diverse matching tasks.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification
The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test.
Transductive Learning for Unsupervised Text Style Transfer
The proposed transductive learning approach is general and effective to the task of unsupervised style transfer, and we will apply it to the other two typical methods in the future.
Adaptive Information Seeking for Open-Domain Question Answering
Information seeking is an essential step for open-domain question answering to efficiently gather evidence from a large corpus.
Ranked #3 on
Question Answering
on HotpotQA
Single Node Injection Attack against Graph Neural Networks
In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i. e., the attacker is only allowed to inject one single node during the test phase to hurt GNN's performance.
Signed Bipartite Graph Neural Networks
Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.
Conditional GANs with Auxiliary Discriminative Classifier
Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively.
Ranked #1 on
Conditional Image Generation
on Tiny ImageNet
INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering
INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table.
Self-Supervised GANs with Label Augmentation
Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment.
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
To capture these properties effectively and efficiently, we propose a novel Recurrent Evolution network based on Graph Convolution Network (GCN), called RE-GCN, which learns the evolutional representations of entities and relations at each timestamp by modeling the KG sequence recurrently.
Locate Who You Are: Matching Geo-location to Text for User Identity Linkage
However, encouraged by online services, users would also post asymmetric information across networks, such as geo-locations and texts.
GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction
However, the low quality of observed user data confuses the judgment on anchor links, resulting in the matching collision problem in practice.
Modelling Universal Order Book Dynamics in Bitcoin Market
Understanding the emergence of universal features such as the stylized facts in markets is a long-standing challenge that has drawn much attention from economists and physicists.
SDGNN: Learning Node Representation for Signed Directed Networks
Guided by related sociological theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks.
Beyond Low-frequency Information in Graph Convolutional Networks
For a deeper understanding, we theoretically analyze the roles of low-frequency signals and high-frequency signals on learning node representations, which further explains why FAGCN can perform well on different types of networks.
Towards Powerful Graph Neural Networks: Diversity Matters
For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.
Learning content and context with language bias for Visual Question Answering
Many works concentrate on how to reduce language bias which makes models answer questions ignoring visual content and language context.
PPGN: Phrase-Guided Proposal Generation Network For Referring Expression Comprehension
Reference expression comprehension (REC) aims to find the location that the phrase refer to in a given image.
Slimmable Generative Adversarial Networks
In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power.
AugSplicing: Synchronized Behavior Detection in Streaming Tensors
Therefore, we propose a fast streaming algorithm, AugSplicing, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step.
Summarizing graphs using configuration model
As a solution, graph summarization, which aims to find a compact representation that preserves the important properties of a given graph, has received much attention, and numerous algorithms have been developed for it.
Social and Information Networks
DeepHawkes: Bridging the gap between prediction and understanding of information cascades
In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.
Cascade Dynamics Modeling with Attention-based Recurrent Neural Network
However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade— one sharing behavior could be triggered by its non-immediate predecessor in the memory chain.
